Authors - Thaw Thaw May Oo, Khaing Khaing Wai Abstract - Modern maritime industry depends largely on digital communications and access control systems for their operation and security maintenance. On the other hand, digital communication and access control systems make maritime industry more vulnerable to cybersecurity attacks, such as unauthorized access, data leaks, and insiders' malicious actions. Centralized security measures become inefficient against modern and advanced cyber threats. In that regard, this paper presents a Smart Digital Lock System using Zero Trust Architecture and AES Encryption. The suggested approach assumes the implementation of zero trust policy in terms of continuous user identity validation requiring tight access control, including strict user authentication and monitoring. Multifactor authentication and real-time monitoring are the key characteristics of the suggested system, especially considering such potential high-risk zones as ships and ports. Communication of authorized parties will be performed using the AES encryption to protect the information's privacy and integrity. As a result, the presented system will be assessed from three perspectives: authentication accuracy, data protection effectiveness, and response latency.
Authors - Ei Marlar Win, Amy Tun, Khant Kyawt Kyawt Theint Abstract - Electrocardiogram (ECG) signal analysis plays an important role in the early detection and diagnosis of cardiovascular diseases. Manual interpretation of ECG recordings is time-consuming and highly dependent on clinical expertise, creating a need for automated and accurate classification systems. This study presents an automated ECG classification model using signal preprocessing, heartbeat segmentation, wavelet, feature extraction, and deep learning. ECG signals are preprocessed to remove noise using filtering and normalization methods. Features are extracted heartbeat segments-based windows around each R peak and classified into five different arrhythmias N (Normal), V (Ventricular), S (Supraventricular), F (Fusion) and Q (Unknown/noisy /unclassified) using wavelet Convolutional Neural Network (CNN) Self Attention model. Experiments on MIT-BIH ECG dataset and analyze the model performance evaluation across a single-lead ECG, multi lead ECG, lead fusion and feature fusion techniques by wavelet attention. The results indicate that the proposed approach yields high classification performance and effectively distinguishes heartbeats abnormalities. Class weighting techniques were applied to address the issue of imbalanced class labels in the ECG dataset. The lead fusion approach achieved classification accuracies of 0.98. Single lead, multi lead and feature fusion experimental approaches were evaluated, resulting in classification accuracies of 0.97, 0.98, and 0.97, respectively. The class-weighting method combined with lead fusion feature extraction obtained an accuracy of 0.95. Furthermore, class weight additional techniques achieved accuracies of 0.91, 0.92 and 0.92, demonstrating variations in model performance across different methodologies. This automated system can support clinicians to assist in the early diagnosis of heart abnormalities and improve healthcare efficiency.
Authors - Caroline Sutiono, Ronald Gunawan, Silvina Chandra, Maria Pia Adiati Abstract - Online to Offline applications (O2O) have transformed a service style to a new level, since consumers increasingly rely on digital technology to access daily food and beverage products and services based on their needs and preferences. Prior to their arrival, the customer browses the menu, place the order and finish the payment and afterwards the product will be collected at the store. The application required to provide details menu information, options and preference as well as payment details. To use of O2O applications requires customers to have sufficient digital literacy to navigate the ap-plication, place orders, and complete upfront payments. Meanwhile, outlet staff must be able to accurately interpret and process each order specification to ensure service accuracy. Therefore, this study is examining the relationship between O2O application usage, service efficiency, and customer experience in F&B retail businesses. This research uses a quantitative research method, with a survey approach with 160 eligible respondents and analyzed thru SEM PLS. This result emphasizes the importance of user experience and service design into interaction in O2O application usage experience, where customers prioritize applications that are intuitive, convenient, and aligned with their needs. Therefore, the effectiveness of O2O applications is influenced not only by operational efficiency but also by how well the technology supports user-friendly and meaningful user experiences.
Authors - Su Thet Oo, Ah Nge Htwe, Nilar Aye Abstract - The automatic detection of AI-generated art images is essential for distinguishing authentic human creations from artificial ones. This process is critical for authenticity verification, provenance control, misinformation management, and digital forensics. With the rapid evolution of deep learning content generation, the existing detection approaches within artistic imagery remain an underexplored domain characterized by artworks that differ widely in style and often contain non-standard, complex, or distorted visual patterns. The proposed model is an empirical study of a fine-tuned CNN-based generative art detection to classify real and human-created art accurately by learning discriminative visual features such as texture, structure, and statistical patterns, adapting a pre-trained CNN model and also finetuning architecture layers and defining the spatial dimension, which is used to determine the level of detail captured in feature extraction and classification. In our system, utilizing a balanced dataset consisting of real and AI-generated art images, the system was trained and evaluated, where a base VGG16 net in traditional architecture and this architecture of pre-trained and fine-tuned VGG16 with hyperparameter tuning of task-specific input representation and data augmentation, layer optimization strategies using the same balanced dataset, with results benchmarked against a strong baseline.
Authors - Ferdinand V. Dalisay, Gerli Ryza DS. Reyes Abstract - On-the-Job Training (OJT) in Philippine higher education institutions (HEIs) stands at a decisive inflection point. Historically constrained by misaligned curricula, weak industry-academe partnerships, and inadequate quality assurance mechanisms, the OJT system is now confronted simultaneously with the disruptive potential of artificial intelligence (AI), the transformative power of data analytics, and the imperatives of broader digital transformation. This systematic literature review synthesizes 35 peer-reviewed studies and policy documents published between 2020 and 2026 to examine how these three technological forces are reshaping and should further reshape the design, implementation, supervision, and evaluation of OJT programs across Philippine colleges and universities. Guided by the TIBS 2026 conference tracks on AI and Intelligent Systems, Data Analytics and Business Intelligence, and Digital Transformation and Technology Strategy, the review constructs a crosscutting analytical framework that interrogates the current state of Philippine OJT against the backdrop of these technological paradigms. Four thematic clusters are identified: (1) AI-mediated supervision, mentoring, and competency scaffolding; (2) data-driven OJT quality assurance and outcome analytics; (3) digital platform ecosystems and virtual work-integrated learning; and (4) strategic alignment between OJT curricula and the emerging digital economy. Findings reveal that while Philippine HEIs have begun to engage with digital tools in OJT administration, deep integration of AI and analytics into OJT pedagogy and governance remains nascent. The review concludes with a multi-stakeholder digital transformation roadmap for the Philippine OJT system, offering implications for CHED policymakers, HEI administrators, industry partners, and technology developers.
Authors - Janssen Emmanuel Jahja, Anderes Gui Abstract - The rapid development of e-commerce also raises the need for new innovations such as Virtual Try-On (VTO) to address the physical limitations of online product evaluation. Nevertheless, the interaction of functional and psychological factors of VTO is poorly understood as influencing its adoption, while their influence on purchase decisions also remains limited. This study investigates these factors with respect to online purchasing intentions. Incorporating an extended Technology Acceptance Model (TAM) with consumer behavior theories, the conceptual model assesses Perceived Ease of Use, Perceived Usefulness, Perceived Enjoyment, Attitude, Personal Innovativeness in IT, and Self-Efficacy. Using a quantitative approach, information was gathered from consumers who shop on e-commerce sites and analyzed using Structural Equation Modeling (SEM). The results show that the hypotheses suggested are well supported. This study contributes theoretically by extending digital retail literature and offers managerial implications for designing VTO features that not only improve the shopping experience but also yield higher sales conversions.
Authors - Jayanthi J, Krishna Kanwar, Divansh Tarun Mittal, Akash Kumar, Srikanta Pradhan, Arun Kumar K Abstract - The problem of financial distress faced by the sandwich generation-who are held responsible for both the elderly parents and dependent children simultaneously, but not accommodated by available tools-motivates this research. In this work, we developed a portfolio intelligence system named WealthBridge that leverages an AI framework, which includes Random Forest model for risk profiling and an LSTM network for market regime detection. While the model accurately classify investors (with 95% accuracy) and market regimes, it forecasts market trends using time series of various features. A fusion engine then provides recommendation for allocation to different portfolio asset classes and investment in particular stock. It is accessible through the deployment of a Streamlit dashboard, making it an efficient tool for data-driven financial planning. The accuracy was assessed with robust performance of models that caters to financial services of the Indian middle income sandwich generation.
Authors - Marybell Materum, Daniel Dasig Jr, Lucila Magalong, Emelyn Libunao, Shirley Padua, Sonia Pascua, Rizza Gerente and Sharon Sanchez Abstract - Broadband infrastructure has become a critical enabler of digital trans formation, technological competitiveness, and economic sustainability across OECD economies. This study proposes a hybrid technological intelligence framework integrating descriptive analytics, temporal trend modeling, compara tive broadband evaluation, and predictive business interpretation using OECD broadband subscription datasets. The dataset comprised 11,324 broadband obser vations covering fixed, mobile, and fiber-optic technologies across multiple countries and annual periods. A quantitative explanatory research design was em ployed using statistical preprocessing, longitudinal analysis, and machine learn ing-oriented analytical procedures to identify broadband growth dynamics and digital infrastructure disparities. Results revealed substantial asymmetry in broadband adoption patterns, with the United States, Japan, Korea, France, and the United Kingdom demonstrating dominant subscription trajectories and accel erated digital infrastructure expansion. Fiber-optic and mobile broadband tech nologies exhibited the highest growth rates, particularly after 2018, reflecting in tensified digital transformation and remote connectivity demands. The findings demonstrate that broadband intelligence analytics can support strategic business forecasting, digital competitiveness evaluation, telecommunications planning, and evidence-based policy formulation within Industry 4.0 and smart governance ecosystems.
Authors - Govind Kumar, Amresh Kumar, Ajeet Singh Abstract - The process of selecting the right Indian city to live in is an extremely crucial one, which can have a huge impact on One’s life, safety, work and happiness every day. However, the tools available today, The kind of websites that tell about a property, or simple map applications, aren’t smart enough. They Do not know what each member of a neighbourhood really wants. This paper introduces Neighbor- Fit, an innovative AI-driven solution that suggests neighborhoods. Based on the actual need of the user. The system has three new ideas, the first of which is: A composite neighborhood suitability score (CNSS) as a six-part score that perates safety, facilities in the area, travel time, cost of living, green areas, and community life; (2) a smart algorithm called Preference-Adaptive Cascade Hybrid (PACH) which alters its style of recommendation according to the amount of recommendation it already has knows about the user; and (3) an explanation system based on LIME which explains to the user in simple words why a neighborhood was suggested. Tests done on 250 PIN codes In three major cities of India, namely, Delhi, Mumbai and Bengaluru, Preci- shows across. sion@10 of 87.3%, Recall@10 of 84.1%, and F1-Score of 85.7% — better than all There were five methods of comparison (p ¡ 0.05). The system reacts in an average of 340ms time even for 50 users using simultaneously.
Authors - John Julius M. Orillana, Loyd S. Echalar Abstract - Mud crab fattening supports aquaculture, local food supply, and income for small-scale farming communities. In container-based culture systems, farmers face two common problems. They need to keep water quality stable. They need to track crab growth on time. Manual monitoring takes time, changes from one checking period to another, and slows response when water conditions shift. These problems affect crab health, survival, and growth. This study developed CRABSMART, a smart container-based fattening system for mud crabs, Scylla serrata, with integrated water quality monitoring and growth prediction. The system tracks temperature, pH, dissolved oxygen, and salinity through sensors linked to a microcontroller platform. The platform sends the data to a web-based dashboard for real-time display, historical monitoring, and system status tracking. The study also includes a growth prediction component. This component estimates growth trends from recorded water quality conditions and culture duration. The study used a developmental research approach for design, integration, and implementation of the prototype. Functional assessment examined sensor operation, data transmission, dashboard performance, and integration of the prediction component. CRABSMART supports faster decisions, reduces manual monitoring, and improves daily management in mud crab fattening. The system provides a practical approach for smart aquaculture, especially in container-based mud crab production.
Authors - Sarita Thummar, Amit Thakkar, Gayatri Patel, Vaishali Koria, Yug Mordiya Abstract - Leukemia is a malignancy that afflicts blood and bone marrow and requires a precise diagnosis and care to be effective. False diagnosis and diagnosis at a late stage result into death. Diagnostic capabilities have been greatly improved by recent developments in Artificial Intelligence (AI), especially machine learning and deep learning. However, many AI models, also known as black boxes, are opaque and thus restricted to use in a clinical scenario where interpretability and transparency is important. This paper will look at the application of Explainable AI (XAI) to diagnose leukemia, with a particular focus on how it can be used to provide clear and intelligible explanations of AI-driven decisions. The experimental results prove that the given ensemble model can be useful in classifying the subtypes of leukemia. Explainable AI methods like SHAP and LIME also enable more trust since the insights obtained are transparent and clinically relevant. This demonstrates the possibility of interpretable models being applicable to practice to aid clinical diagnosis. By using XAI techniques on trained model, the potential of XAI to bridge the gap between high-performance AI and clinical applicability is demonstrated. Despite its potential, XAI is faced with several challenges to address, including the need to integrate it into existing clinical workflows, technical complexity, and issues of data protection. At the end of the paper, the importance of developing domain-specific XAI methods and collaborative structures to succeed is outlined.
Authors - Carolina Ditan, Daniel Dasig Jr, Sushil Kumar Singh, Isagani Valenzuela II, Catherine Catalan, Bablu Khumar Dhar, Jewelyn Ciocon and Maricris Ediza Abstract - The increasing complexity of sustainable governance ecosystems re quires advanced analytical models capable of integrating multidimensional soci oeconomic, environmental, governance, and technological indicators into inter pretable strategic intelligence systems. This study proposes a Federated Techno logical Intelligence Framework (FTIF) utilizing the World Bank Sustainable and Social Governance Database (WB_SSGD) to analyze governance resilience, en vironmental sustainability, institutional effectiveness, and digital transformation patterns across multiple countries. The study integrates explainable artificial in telligence (XAI), federated analytics, ensemble machine learning, and nonlinear predictive modeling to identify strategic relationships among governance indica tors, energy transition variables, democratic participation metrics, and environ mental sustainability indicators. The methodology combines Random Forest Re gression, Gradient Boosting Machines, Long Short-Term Memory (LSTM) tem poral learning, SHAP explainability mechanisms, and panel-based econometric validation. Findings reveal that governance effectiveness, access to civil justice, corruption control, democratic participation, and carbon intensity significantly influence sustainable development trajectories. The hybrid architecture achieved high predictive reliability with strong convergence stability and reduced predic tion variance across heterogeneous country clusters. The SHAP-based explaina bility analysis further demonstrates that institutional quality variables contribute more significantly to sustainability outcomes than isolated economic indicators. The proposed framework contributes to technological intelligence literature by introducing a scalable and interpretable governance analytics architecture for strategic policymaking and digital sustainability planning. The study offers prac tical implications for governments, higher education institutions, business strate gists, and international development organizations pursuing evidence-based gov ernance transformation.
Authors - Alyssa C. Vicente, Cedirick Santiago, Elmer M. Alino, Ma. Yvonne Czarina C.Angcaya, Benedict G. Bautista, St. Joseph M. Lumbog Abstract - This systematic review investigates the application of machine learning (ML) and deep learning (DL) in the early detection of Autism Spectrum Disorder (ASD), a neurodevelopmental condition characterized by social and communication deficits. Adhering to the 24-step framework by Muka et al. and PRISMA 2020 guidelines, the methodology involved a rigorous search of four academic databases—IEEE Xplore, Scopus, PubMed, and ACM Digital Library— identifying 67 records. Ultimately, 10 peer-reviewed studies published between 2020 and 2024 were analyzed based on their use of real-world datasets and quantitative metrics. Results indicate that ML models, particularly Convolutional Neural Networks (CNNs) and ensemble classifiers, achieve high predictive performance with accuracies between 80% and 94%. The findings highlight that behavioral data from home videos and eye-tracking scan paths serve as effective indicators for remote, scalable screening. However, the review identifies significant gaps, including small, homogeneous datasets and a lack of model interpretability. To advance the field, future research must focus on Explainable AI (XAI), multimodal fusion, and the development of large-scale, multicultural, open-access datasets to ensure clinical trust and global generalizability.
Authors - Takumi Kato Abstract - According to Processing Fluency Theory, the more fluently people can process an object, the more positive their aesthetic response becomes, making symmetrical designs more desirable. Furthermore, symmetry is also expected in the context of ethical products, as simplicity is effective in fostering an impression of environmental and health considerations. However, symmetry is a highly symbolic and essential design. Based on Construal Level Theory, people prefer essential objects when they feel a greater psychological distance from them, and prefer objects when they feel a greater psychological distance. Through this theoretical lens, the evaluation of essential symmetrical designs may differ depending on the psychological distance from the product. This study posed the research question: "Do people who feel a greater psychological distance from the product rate products with symmetrical designs more highly than those who feel a greater psychological distance?" Focusing on detached houses, a randomized controlled trial was conducted with 1,000 Japanese people aged 20-60. The results showed that in detached house designs, symmetrical designs were significantly more favorably received than asymmetrical designs in terms of living intention, healthy impression, and environmental impression. However, these effects were more pronounced in people living in apartments than in those currently living in detached houses. Therefore, it can be inferred that symmetry is more effective for luxury goods than for inexpensive goods, for gifts to others than for personal use, and for goods that will be useful in the future than for goods that will be useful immediately.
Authors - Jemima Achiah, Benjamin Ghansah, Stephen Opoku Oppong, Charles Buabeng Andoh, Joseph Kwabena Essibu, Christopher Yarkwah Abstract - The integration of digital literacy within basic education has become increasingly important in preparing learners with the competencies required for participation in twenty-first-century society. This study investigates how basic school teachers in Ghana foster learners’ dig-ital literacy competencies within the context of the Standards-Based Curriculum. Specifically, the study examines the instructional strategies employed by teachers, the contextual challenges influencing implementation, and the extent to which these practices shape learner engagement and digital skill acquisition. An embedded mixed-methods research design was adopted, com-bining qualitative and quantitative approaches to provide a comprehensive understanding of classroom practices and learner experiences. Qualitative data were collected through semi-struc-tured interviews with six teachers and observations of school digital infrastructure, while quan-titative data were obtained from 122 learners across three public basic schools in Komenda, Ghana. The findings revealed that teachers predominantly employed learner-centered pedagogi-cal approaches, including hands-on instruction, collaborative learning activities, and the integra-tion of learner-owned digital devices to facilitate practical engagement. Despite persistent chal-lenges relating to inadequate infrastructure, limited access to digital resources, and insufficient professional development opportunities, these instructional practices contributed positively to learners’ motivation, confidence, and practical ICT competencies. The study contributes to the limited empirical literature on teacher-driven digital literacy development within Ghanaian basic education and highlights the critical need for sustained teacher capacity building, improved dig-ital infrastructure, and supportive policy interventions to strengthen effective digital literacy in-tegration in resource-constrained educational contexts.
Authors - Shivam Kumar, Dinesh Kumar Saini Abstract - KG-RAG (Knowledge Graph-Retrieval Augmented Generation) is an advanced AI framework that combines structural knowledge graphs with LLMs to make them smarter, more accurate, robust, and less prone to hallucination. However, existing KG-RAG pipelines are often tightly coupled with specific domains. In addition, most of the systems lack proper schema validation and have limited support for temporal knowledge. GenericKG is a modular framework designed to decouple knowledge ingestion, validation, storage and retrieval across domains. The framework includes an agentic ingestion pipeline with schema-driven knowledge graph construction, supported by multi-level validation (L1-L3) to ensure structural, semantic and temporal consistency. Temporal attributes and semantic embeddings are integrated at framework level, enabling time-aware querying and hybrid retrieval without domain-specific reengineering. This paper is evaluated on three benchmarks: the BC5CDR biomedical corpus (87.92% entity F1 with 100% precision), the WebNLG crossdomain dataset (85.6% entity F1 across 15+ relation types on 100 records), and HotpotQA multi-hop question answering (58.0% accuracy on bridge and comparison questions). A raw-LLM baseline without schema guidance scores 0% on all metrics, confirming the importance of the schemadriven pipeline. This framework is implemented in TypeScript and it will be released as open source.
Authors - Abraham Gezehei, Thomas Hanne, Rolf Dornberger Abstract - This study benchmarks twelve recurrent neural network (RNN) architectures for univariate macroeconomic time-series forecasting, covering LSTM and GRU baselines, width/depth scaling, bidirectional encoders, an attention-like pooling variant, convolutional–recurrent hybrids, and strong regularization. Following the Libra benchmarking philosophy and the multi-metric evaluation advocated by Prater et al., we compare all configurations under identical protocols on 100 series from the Libra Economics collection. A bidirectional GRU yields the best RNN accuracy (sMAPE 41.0, MASE 0.0447), improving over a comparable 2-layer GRU baseline (sMAPE 41.9) at higher wall-clock runtime. Most architectural additions and capacity increases do not improve performance over the simple GRU baseline (e.g., deeper/wider models, pooling-based attention, CNN–RNN hybrids, and heavy dropout). The results suggest that short input windows (dynamically sized at 10% of series length, minimum 10 steps) limit the benefits of architectural complexity in this setting. Classical statistical methods (sNaive, ETS, Theta) outperform all neural models by a wide margin while requiring substantially less computation. For these low-frequency macroeconomic series, shallow GRU variants—especially bidirectional encoders—are the strongest RNN option, but classical baselines remain the practical choice.
Authors - Alfito Athar Rayyansyah, Abdurrahman Faris Indriya Himawan, Galuh Sudarawerti Abstract - Governance challenges remain a major concern in large-scale procurement activities, particularly regarding transparency, accountability, and operational effectiveness. This study investigates the role of Big Data Analytics (BDA) and Information Processing Capability (IPC) in enhancing governance outcomes within the e-procurement environment of PT PLN Indonesia Power. Specifically, the study examines how these capabilities contribute to transparency and accountability and how they affect both financial and non-financial procurement performance. A quantitative research design was employed, and data were gathered from employees engaged in procurement-related activities. The proposed model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that all proposed hypotheses are statistically supported. BDA emerged as the primary factor driving transparency and accountability, which subsequently improves procurement performance, particularly non-financial outcomes. The findings reveal that IPC serves as a key enabler in maximizing the value of BDA while increasing the ability of e-procurement systems to support data-driven analysis. These findings offer practical implications for state-owned enterprises by emphasizing the importance of integrating analytical capabilities and information-processing resources to strengthen governance quality and improve procurement effectiveness in digital environments.
Authors - Ichwan Masnadi, Renza Fahlevi, Elda Nurmalinda Abstract - The purpose of this study is to analyze how Perceived usefulness of smart lighting can affect Revisit Intention through the mediation of Green Hotel Image. This study was conducted on hotel guests who stayed at hotels that implemented smart lighting technology in Jakarta. This study uses quantitative methods by sending questionnaires online via Google Forms to 150 hotel guests who have previously stayed at hotels with smart lighting technology implemented. The data was then processed using SEM-PLS (Structural Equation Modeling–Partial Least Square) through SmartPLS 3 software. The results showed that Perceived usefulness of smart lighting had a positive and significant impact on Green Hotel Image. Green Hotel Image also had a positive and significant effect on Revisit Intention. Perceived usefulness of smart lighting had no effect on Revisit Intention. Furthermore, results from the analysis showed that Green Hotel Image fully mediated the effect of Perceived usefulness of smart lighting on Revisit Intention. In conclusion, guests are not inclined to revisit hotels that implement smart technology such as smart lighting. Smart technology indirectly fulfills its role by increasing the hotel’s green (environment-friendly and sustainability focused) image which leads to customer revisit intention. This study contributes to the SOR Theory by showing how Perceived usefulness of smart lighting is the Stimulus factor, Green Hotel Image is the Organism factor, and Revisit Intention is the Response factor. Hotel managers can benefit from this study by properly branding their hotels’ sustainability to leverage their use of smart technology in order to compete with other hotels.
Authors - Luong Vinh Quoc Danh, Truong Minh Nhan, Nguyen Tan Dat, Nguyen Vinh Thanh, Do Chi Tam, Le Tan My, Nguyen Chanh Nghiem Abstract - Accurate avocado ripeness assessment is essential for ensuring product quality and effective postharvest management, yet conventional evaluation methods remain largely destructive, time-consuming, and limited to representative samples. This paper presents a non-destructive ripeness assessment method combining microwave sensing with feedforward neural network (FNN) classification. A custom-designed open-ended coaxial probe connected to a vector network analyzer was employed to measure the complex reflection coefficient S11 of avocado samples over a frequency range of 1.1–3.1 GHz. Variations in the dielectric properties of avocado flesh during ripening produce corresponding and measurable changes in the S11 characteristics, from which magnitude, phase, and frequency features were extracted and used as inputs to the FNN classifier. The proposed system achieved an overall classification accuracy of 87% in discriminating among three ripeness stages – unripe, ripe, and overripe – thereby demonstrating its viability as a rapid, costeffective, and non-destructive alternative to conventional destructive ripeness assessment methods.
Authors - Christian Vasta, Rolf Dornberger, Thomas Hanne Abstract - The Inventory Routing Problem (IRP) is a critical challenge in logistics, combining vehicle routing with inventory management under a unified objective. Recent research in computational intelligence has advanced the use of metaheuristics for tackling such combinatorial problems. Among these, Simulated Annealing (SA) remains underexplored for IRP compared to more commonly applied methods. In this study, we address this gap by implementing a custom SA algorithm to solve a deterministic five-day IRP. The goal is to minimize total transportation costs while satisfying daily customer demand using a single-vehicle fleet with fixed capacity. The algorithm's performance is evaluated with 20 independent runs and compared to a modified Tabu Search benchmark using the same deterministic instance. Our results show that Simulated Annealing performs competitively, producing high-quality solutions, with moderate variation observed across different cooling schedules and repeated runs. Although it shows greater sensitivity to initial parameters and stochastic behavior, its exploratory nature allows it to overcome local optima more effectively than Tabu Search in some cases. The outcomes suggest that SA is a viable alternative for IRP under deterministic conditions, particularly when flexibility in parameter tuning is prioritized.
Authors - Febin Koshy Jacob, Indranil Bose, Sarika D Tavhare, Sandhya Anilkumar Abstract - Modern automotive Electronic Control Unit (ECU) systems demand robust and accurate validation frameworks to address increasing system complexity while minimizing manual test effort and development cost. This paper novels an automated Hardware-in-the-Loop (HIL) testing framework for validation of automotive systems, with a primary focus on automated waveform pattern analysis method. The framework integrates a dSPACE real-time interface with a hardware test bench and algorithm developed using a MATLAB-based simulation model of the Body Control Module (BCM) to generate and analyze input signals. Python-based automation scripts are utilized for test execution control, synchronized data acquisition, and automated result analysis, ensuring repeatable and scalable testing across multiple application domains. The core contribution is a reference-driven waveform comparison methodology, where signals captured from the Device Under Test (DUT) are evaluated against predefined golden reference waveforms. The approach quantifies Root Mean Square Error (RMSE) percentage and timing deviations across individual channels, enabling precise detection of mismatches in waveform sequences. The framework is demonstrated through automotive tail lamp animation pattern validation, where output sequences are compared against reference waveforms for accuracy and robust assessment. Additionally, the solution is extendable to electric vehicle subsystems such as Battery Management Systems (BMS), Traction Motor Control Units (TMCU), and Off-Board Chargers (OFBC), supporting both dynamic and steady-state validation such as torque-speed curve, Battery profile testing, Sensor accuracy etc. The implementation achieves approximately 45.8% automation of test cases and reduces overall validation time by about 41.2%, resulting in improved repeatability, reduced manual intervention, and faster development cycles, ultimately enabling faster time-to-customer and providing a scalable and efficient solution for modern automotive and electric vehicle system validation.
Authors - Apolinar P. Datu, Jeferson C Mojica, Pamela Daphne R. Busog, Kelvin M. Custodio, Desiree Anne D. Mendoza, Kristel Shane C. Paminter, Rose Ann T. Genova, Keno A. Villavicencio Abstract - This study explores how on-the-job training (OJT) helps student interns improve their ability to work with others. It focuses on how real workplace exposure strengthens teamwork, communication, and adaptability. Data were collected from 150 interns from different academic programs using a survey that examined their experiences during training. The findings show that most interns felt a noticeable improvement in their collaborative skills. Many were actively involved in meetings, team activities, and workplace discussions, which gave them valuable opportunities to interact and contribute. These experiences not only helped them communicate more confidently but also made them more comfortable working as part of a team. The results also indicate that supportive work environments—those that encourage communication and teamwork—play an important role in helping interns grow. In addition, OJT helped boost their confidence, sense of responsibility, and readiness for future employment. Overall, the study highlights the importance of OJT as a bridge between academic learning and real-world practice. It reinforces the idea that hands-on experience is essential in preparing students for a workplace that values collaboration and adaptability.
